Why AI Needs A New Funding Phase
Entrepreneurship
AI companies are stalling in a post-build, pre-scale phase. The bottleneck is not capability but integration, behaviour change and trust — and no single source of capital is built for it.
Artificial intelligence is entering a phase that most venture funding models were never designed to support. Early AI companies were funded to prove technical feasibility; later-stage companies are funded to scale. A growing number of startups are now stalling in between. As AI products move from impressive early products into scalable organisations, the source of value creation shifts. Model capability matters less; integration, behaviour change and trust matter more so that you can unlock enterprise value. Customers are not simply buying software. They are being asked to change how decisions are made, how work is organised and how accountability is distributed. That takes time, training and experimentation, and it creates a phase of company-building that is capital-intensive, slow to signal success and still poorly understood by investors.
The traditional venture playbook works well for software that slots neatly into an existing workflow: build the product, sell it, scale it. AI is transformative and therefore behaves differently when scaling it to enterprise. Pre-seed and seed capital prioritise shipping and early traction, while growth investors expect predictable expansion. The difficult work required to move from one state to the other — educating users, redesigning workflows, building confidence in AI-supported decisions and adapting the product around real-world behaviour — is often treated as overhead, or assumed to resolve itself.
This creates a missing middle in AI investing: a post-build, pre-scale phase where otherwise strong companies can look weaker than they really are. Slow usage may be read as weak demand, friction as poor product-market fit, and early churn as a market signal rather than the cost of introducing a new way of working. Yet many AI companies are not failing because their products lack value; they are stalling because their customers do not yet have the capacity to adopt them quickly.
The uncomfortable part is that no single source of capital is especially well designed for this phase. Angels can be essential early believers but rarely have the mandate for slower, non-linear progress once a product is built. Traditional venture is good at scaling proven patterns but often uncomfortable with ambiguous traction or no traction while navigating the enterprise adoption gap. Corporate investors can bring domain knowledge and integration experience, but may move slowly or introduce different strategic incentives. Founders are left caught between investors expecting speed and customers needing time.
The more interesting question may be whether this integration phase creates space for new funding and operating models altogether. Some of the value may come from deeper customer partnerships, co-build arrangements or customers effectively funding part of the adoption journey through paid pilots, implementation work or strategic collaboration. There may also be a larger role for venture operators, specialist funds or private-equity-style models that are more comfortable with operational complexity, longer integration periods and hands-on value creation than traditional venture capital. None of these is necessarily the answer on its own, but the gap suggests an opportunity for capital structures that are better matched to how AI businesses actually move from technical capability to embedded use.
This is not an argument for lowering standards or subsidising weak companies. It is an argument for recognising that AI often creates new costs before it creates new returns, and that capital structures may need to reflect that reality. During this integration phase, the most useful signals may not be headline growth metrics, but learning velocity, depth of engagement, quality of implementation and whether the company is building the conditions for lasting adoption. This is in line with the change in thinking between the lean startup thinking of the 2000s and the build well thinking we are now moving into, when trust, security and stability are seen as a differentiator in a landscape full of sloppy products.
Investors who understand that may have an edge. Supporting companies through the messy integration period rather than pulling back at the first sign of friction creates the possibility of participating in the compounding that follows. More importantly, it reduces the risk of writing off strong businesses simply because they are solving real problems on timelines the market has not yet adjusted to.
AI is not just another software cycle. It is a general-purpose technology that changes how work gets done. Treating it as though it will follow familiar adoption curves risks systematic mispricing and missing opportunities for massive value creation. The question is whether existing funding models are prepared to support the transition.